Modulation Recognition Method for High-Speed Mobile Communication Based on Attention Dynamic Fusion and Hybrid Pruning Transformer

ZHENG Qinghe; CHEN Bin; YU Lisu; HUANG Chongwen; JIANG Weiwei; SHU Feng; ZHAO Yizhe

doi:10.11999/JEIT251211

Article Contents

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ZHENG Qinghe, CHEN Bin, YU Lisu, HUANG Chongwen, JIANG Weiwei, SHU Feng, ZHAO Yizhe. Modulation Recognition Method for High-Speed Mobile Communication Based on Attention Dynamic Fusion and Hybrid Pruning Transformer[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251211

Citation:

ZHENG Qinghe, CHEN Bin, YU Lisu, HUANG Chongwen, JIANG Weiwei, SHU Feng, ZHAO Yizhe. Modulation Recognition Method for High-Speed Mobile Communication Based on Attention Dynamic Fusion and Hybrid Pruning Transformer[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251211

Citation:

ZHENG Qinghe, CHEN Bin, YU Lisu, HUANG Chongwen, JIANG Weiwei, SHU Feng, ZHAO Yizhe. Modulation Recognition Method for High-Speed Mobile Communication Based on Attention Dynamic Fusion and Hybrid Pruning Transformer[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251211

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Modulation Recognition Method for High-Speed Mobile Communication Based on Attention Dynamic Fusion and Hybrid Pruning Transformer

doi: 10.11999/JEIT251211 cstr: 32379.14.JEIT251211

1.
School of Intelligent Engineering, Shandong Management University, Jinan 250357, China
2.
School of Information Science and Engineering, Hefei University of Technology, Hefei 230000, China
3.
School of Information Engineering, Nanchang University, Nanchang 330031, China
4.
School of Information and Electronic Engineering, Zhejiang University, Hangzhou 310058, China
5.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
6.
School of Information and Communication Engineering, Hainan University, Haikou 570228, China
7.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Natural Science Foundation of China (62401070), The Shandong Provincial Natural Science Foundation (ZR2023QF125, ZR2019ZD01), The Shandong Provincial Youth Innovation Team Plan of Higher Education Institutions (2024KJH005), The Shandong Provincial Science and Technology Based Small and Medium Sized Enterprises Innovation Capability Enhancement Project (2024TSGC0055)

Accepted Date: 2026-02-11
Rev Recd Date: 2026-02-11

Available Online: 2026-03-01

Abstract

Abstract

Objective Automatic modulation recognition is a critical preprocessing step in dynamic spectrum access and anti-jamming communication systems, directly impacting the robustness and spectrum efficiency of non-cooperative communication. In high-speed mobile communication scenarios such as satellite, high-speed rail, and drone swarm communications, signal modulation features suffer severe distortion due to Doppler shifts, time-varying channels, and non-stationary interference. The above issues pose significant challenges to traditional modulation recognition methods based on static assumptions, leading to feature mismatch and increased misjudgment rates. To address the issues of insufficient robustness and real-time performance in existing deep learning-based modulation recognition models under high-speed mobile environments, this paper proposes a lightweight dynamic fusion Transformer-based approach. Methods The proposed method consists of three main components: signal representation fusion block, Transformer model design, and model pruning for lightweight inference. First, a RollingQ mechanism is introduced to dynamically adjust the direction of attention query matrix based on the quality of each signal representation, breaking the cycle of attention fixation and achieving the balanced utilization of all types of signal representations. Then, the multi-head attention frequency enhancement Transformer (MAFE-Transformer) is designed, which integrates local and global spatiotemporal features through modules including lightweight convolutional enhancement, multi-attention feature extraction, and frequency learning and selection. Finally, an attention-based dynamic hybrid pruning strategy is applied to reduce structural redundancy and accelerate inference, enabling real-time modulation recognition. Results and Discussions Extensive experiments are conducted on two public datasets, RadioML 2016.10a and RML22, to validate the effectiveness of the proposed method. The MAFE-Transformer achieves average classification accuracies of 65.14% and 78.40% on the two datasets, respectively. Under low SNR conditions of –20～0 dB, the model demonstrates strong robustness, particularly on the RML22 dataset with dynamic channel model ETU70 (Fig. 5). The confusion matrix shows that the error distribution of MAFE-Transformer is relatively uniform among different modulation schemes, reflecting its well-balanced classification performance (Fig. 6). Ablation studies confirm that the RollingQ-based dynamic fusion mechanism improves accuracy by 7.2% on RadioML 2016.10a and 9.5% on RML22 compared to single signal representation (Fig. 7). The hybrid pruning strategy reduces inference latency to 2.2 ms per signal while maintaining high accuracy (Fig. 8). Comparative experiments show that the proposed model outperforms several state-of-the-art deep learning models (e.g., Ms-RaT, MobileViT, MobileRaT, and KA-CNN) by 4%–10% in recognition accuracy, demonstrating superior performance in high-speed mobile communication scenarios (Fig. 9). Conclusions This paper proposes a lightweight dynamic fusion Transformer-based automatic modulation recognition method to address the challenges of robustness and real-time performance in high-speed mobile communication environments. By introducing RollingQ mechanism and the MAFE-Transformer structure combined with dynamic hybrid pruning, the proposed method achieves a better trade-off between recognition accuracy and inference efficiency. Experimental results on public datasets confirm its effectiveness and robustness under complex channel conditions with Doppler shifts and time-varying interference. However, the proposed method has not been systematically evaluated under more complex interference such as impulsive noise or frequency-selective fading. Future work will focus on improving adaptability to non-stationary noise, cross-device generalization, and optimization for edge deployment.
- High-speed mobile communication,
- Automatic modulation recognition,
- Doppler shift,
- Deep learning,
- Sparse attention mechanism

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References(25)

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